Robust directional-diffusive hybrid molecular communication with parity-check erasure coding

Egashira, Hiroaki; Suzuki, Junichi; Mitzman, Jonathan S.; Nakano, Tadashi; Fukuda, Hiroaki

doi:10.1109/ifsa-scis.2017.8023282

Cited by 5 publications

(6 citation statements)

References 27 publications

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“…Finally, the code word consists of m + k packets, which are transmitted to the receiver. The simulation results in [228] demonstrate that the protocol implemented in [228] is robust for DBMC systems and can reduce latency and jitter [65].…”

Section: ) Egldpcmentioning

confidence: 91%

“…This subsection is for completeness and covers miscellaneous codes that have been surveyed in [65]: parity check erasure [228] (which is a parity check code) and fair weak flip [229] (which is a binary non-linear code), including also the single parity check [227] (which is a parity check code), the EGLDPC [133], and minimum energy channel codes [204].…”

Section: B Miscellaneous Channel Error Mitigating Codesmentioning

confidence: 99%

“…3) Parity Check Erasure Egashira et al [228] introduce parity check erasure coding. Here, the transmitter performs XOR operations on a group of m molecular packets to generate k fixed-length parity bits and k additional packets [228]. These k additional packets are named parity packets and contain the parity codes.…”

Section: ) Egldpcmentioning

confidence: 99%

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Coding in Diffusion-Based Molecular Nanonetworks: A Comprehensive Survey

et al. 2023

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Diffusion-based molecular nanonetworks exploit the diffusion of molecules, e.g., in free space or in blood vessels, for the purpose of communication. This article comprehensively surveys coding approaches for communication in diffusion-based molecular nanonetworks. In particular, all three main purposes of coding for communication, namely source coding, channel coding, and network coding, are covered. We organize the survey of the channel coding approaches according to the different channel codes, including linear block codes, convolutional codes, and inter-symbol interference (ISI) mitigation codes. The network coding studies are categorized into duplex network coding, physical-layer network coding, multi-hop nanonetwork coding, performance improvements of network-coded nanosystems, and network coding in mobile nanonetworks. We also present a comprehensive set of future research directions for the still nascent area of coding for diffusion-based molecular nanonetworks; specifically, we outline research imperatives for each of the three main coding purposes, i.e., for source, channel, and network coding, as well as for overarching research goals.

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Section: ) Egldpcmentioning

confidence: 91%

Section: B Miscellaneous Channel Error Mitigating Codesmentioning

confidence: 99%

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Coding in Diffusion-Based Molecular Nanonetworks: A Comprehensive Survey

et al. 2023

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“…One type of error has not been extensively considered and that is the probability of erasure [19]. A molecule may be erased by a number of mechanisms, such as [20] [21]: (i) absorption by another unintended bio-receiver (i.e.…”

Section: Molecule-scarce Nano-systemmentioning

confidence: 99%

“…For the first experiment, the (19,11) Hamming-LT code is studied (i.e. K = 11, M = 4, Ω = 4), respectively using the proposed SC decoding algorithm and the hard-decision decoding scheme.…”

Section: Sc Decoding Vs Hard-decision Decodingmentioning

confidence: 99%

Hamming–Luby rateless codes for molecular erasure channels

Wei

et al. 2020

Nano Communication Networks

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Nano-scale molecular communications encode digital information into discrete macro-molecules. In many nano-scale systems, due to limited molecular energy, each information symbol is encoded into a small number of molecules. As such, information maybe lost in the process of diffusion-advection propagation through complex topologies and membranes. Existing work has considered Hamming-distance codes for additive counting noise and Hamming-weight codes for transposition errors. Current linear-group codes are not well suited to combating the aforementioned erasure errors. In this paper, we design a novel lowcomplexity erasure combating encoding scheme: the rateless Hamming-Luby Transform code. The proposed rateless code combines the superior efficiency of Hamming codes with the performance guarantee advantage of Luby Transform (LT) codes. We design an iterative soft decoding scheme via successive cancelation to further improve the performance. Numerical simulations show this new rateless code can outperform both standard LT and cascaded Hamming-LT (Raptor) codes, while incurring a lower decoder computational complexity, which is useful for the envisaged resources constrained nano-machines.

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